import numpy as np
import matplotlib.pyplot as plt

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def deriv_sigmoid(x):
    # Sigmoid 函数的求导公式
    fx = sigmoid(x)
    return fx * (1 - fx)

def mse_loss(y_true, y_pred):
    # 均方误差作为损失函数
    return ((y_true - y_pred) ** 2).mean()

class OurNeuralNetwork:
    def __init__(self):
        # 权值初始化
        self.w1 = np.random.normal()
        self.w2 = np.random.normal()
        self.w3 = np.random.normal()
        self.w4 = np.random.normal()
        self.w5 = np.random.normal()
        self.w6 = np.random.normal()
        # 偏置初始化
        self.b1 = np.random.normal()
        self.b2 = np.random.normal()
        self.b3 = np.random.normal()

    def feedforward(self, x):  # 前向传播
        # x is a numpy array with 2 elements.
        h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)
        h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)
        o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)
        return o1

    def train(self, data, all_y_trues):
        # 学习率和迭代次数的定义
        learn_rate = 0.1
        epochs = 1000  # number of times to loop through the entire dataset
        losses = []  # 记录每个epoch的损失

        for epoch in range(epochs):
            for x, y_true in zip(data, all_y_trues):
                # --- 前向传播
                sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1
                h1 = sigmoid(sum_h1)
                sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2
                h2 = sigmoid(sum_h2)
                sum_o1 = self.w5 * h1 + self.w6 * h2 + self.b3
                o1 = sigmoid(sum_o1)
                y_pred = o1

                # --- 计算偏导数
                d_L_d_ypred = -2 * (y_true - y_pred)

                # Neuron o1
                d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)
                d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)
                d_ypred_d_b3 = deriv_sigmoid(sum_o1)

                d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)
                d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)

                # Neuron h1
                d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)
                d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)
                d_h1_d_b1 = deriv_sigmoid(sum_h1)

                # Neuron h2
                d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)
                d_h2_d_w4 = x[1] * deriv_sigmoid(sum_h2)
                d_h2_d_b2 = deriv_sigmoid(sum_h2)

                # --- 更新权重和偏置
                # Neuron h1
                self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1
                self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2
                self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1

                # Neuron h2
                self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3
                self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4
                self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2

                # Neuron o1
                self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5
                self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6
                self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3

            # --- 计算每个epoch的损失并记录
            y_preds = np.apply_along_axis(self.feedforward, 1, data)
            loss = mse_loss(all_y_trues, y_preds)
            losses.append(loss)
            if (epoch + 1) % 10 == 0:
                print("Epoch %d loss: %.3f" % (epoch + 1, loss))

        # 绘制损失曲线
        plt.plot(losses)
        plt.title('Loss over epochs')
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.show()

# 定义数据集
data = np.array([
  [-2, -1],  # Alice
  [25, 6],   # Bob
  [17, 4],   # Charlie
  [-15, -6], # Diana
])
# 真实值的数据集
all_y_trues = np.array([
  1, # Alice
  0, # Bob
  0, # Charlie
  1, # Diana
])
# 开始训练
network = OurNeuralNetwork()
network.train(data, all_y_trues